The Text Mining Approach to Identifying What Students Value in Mathematics Learning

Knowing students’ values of mathematics learning is important for effective instruction. There has been a lack of studies analysing students’ descriptive responses in the values-in-mathematics-education research community, however. Against this trend, this chapter presents a text mining approach, specifically Term Frequency-Inverse Document Frequency analysis and Centrality analysis along with a network graph (Berry, Computi Rev 45:548, 2004; Newman, New Palgrave Encyclopedia Econ 2:1–12, 2008), to extract meaningful patterns or relationships from the unstructured student data. This chapter compared and contrasted students’ general values of learning mathematics, their personal values of learning mathematics, and their perceived teacher values of teaching mathematics. The frequency analysis of students’ values resulted in the top three words, problem, understanding, and review, in common. The noticeable similarities between students’ personal values of learning mathematics and their perceived teacher values of teaching mathematics indicate implications on the interactions between teacher values and students’ values. As such, this chapter identifies the mathematics educational values embraced by Korean students as well as presents a new methodological approach through text mining to analyse values.